Event-Triggered <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mi>μ</mml:mi></mml:math>-state estimation for Markovian jumping neural networks with mixed time-delays

Zou, Cong; Li, Bing; Liu, Feiyang; Xu, Bingrui

doi:10.1016/j.amc.2022.127056

Cited by 4 publications

(3 citation statements)

References 36 publications

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“…In recent years, neural networks have stirred a great deal of research attention [11][12][13][14][15][16][17][18][19]. Meanwhile, with its rapid development, convolutional neural network (CNN) has been widely used in image recognition [20,21], speech signal disposal [22], image denoising [23] and other fields, which have attracted the attention of researchers, see e.g., [24][25][26][27][28][29][30][31][32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Denoising Method for Microseismic Signals with Convolutional Neural Network Based on Transfer Learning

Feng

Guo³

et al. 2023

Int J Comput Intell Syst

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Microseismic signals contain various information for oil and gas developing. Increasing the signal-to-noise ratio of microseismic signals can successfully improve the effectiveness of oil and gas resource exploration. The lack of sufficient labeled microseismic signals makes it difficult to train neural network model. Transfer learning can solve this problem using image data sets to pre-train the denoising model and the learned knowledge can be transferred into microseismic signals denoising. In addition, a convolutional neural network (CNN) model with 16 layers is designed for noise reduction. Considering the strong similarity between noisy signals and denoising signals, residual learning is utilized to optimize the denoising model. The simulation experiment results show that the proposed denoising model eliminates the noise in the microseismic signals effectively and quickly, restores the amplitude of the microseismic signals with high accuracy, and has excellent effect in denoising on the information at the edge.

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Section: Introductionmentioning

confidence: 99%

Denoising Method for Microseismic Signals with Convolutional Neural Network Based on Transfer Learning

Feng

Guo³

et al. 2023

Int J Comput Intell Syst

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“…Meanwhile, Markovian-jump time-varying delayed neural networks (MJTDNNs) are a type of neural network that incorporates a Markovian-jump process into their dynamics. This allows the network to switch between different modes of operation, depending on the system's present condition [4][5][6]. The Markovian-jump process is a mathematical model that describes how the system state changes over time, with different modes or states having different dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…The Markovian-jump process is a mathematical model that describes how the system state changes over time, with different modes or states having different dynamics. The transmission of axonal signals usually creates delays in all neural networks, causing unexpected changing network phenomena, such as oscillation and instability [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Saravanan¹,

Vadivel

Gunasekaran

2023

Mathematics

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In this paper, we present a finite-time synchronization (FTS) for quantized Markovian-jump time-varying delayed neural networks (QMJTDNNs) via event-triggered control. The QMJTDNNs take into account the effects of quantization on the system dynamics and utilize a combination of FTS and event-triggered communication to mitigate the effects of communication delays, quantization error, and efficient synchronization. We analyze the FTS and convergence properties of the proposed method and provide simulation results to demonstrate its effectiveness in synchronizing a network of QMJTDNNs. We introduce a new method to achieve the FTS of a system that has input constraints. The method involves the development of the Lyapunov–Krasovskii functional approach (LKF), novel integral inequality techniques, and some sufficient conditions, all of which are expressed as linear matrix inequalities (LMIs). Furthermore, the study presents the design of an event-triggered controller gain for a larger sampling interval. The effectiveness of the proposed method is demonstrated through numerical examples.

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Dynamic event-triggered fault estimation for complex networks with time-correlated fading channels

Dong

Shen

et al. 2023

Information Sciences

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Event-Triggered μ-state estimation for Markovian jumping neural networks with mixed time-delays

Cited by 4 publications

References 36 publications

Denoising Method for Microseismic Signals with Convolutional Neural Network Based on Transfer Learning

Denoising Method for Microseismic Signals with Convolutional Neural Network Based on Transfer Learning

Finite-Time Synchronization of Quantized Markovian-Jump Time-Varying Delayed Neural Networks via an Event-Triggered Control Scheme under Actuator Saturation

Dynamic event-triggered fault estimation for complex networks with time-correlated fading channels

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